System and method for automated angiography utilizing a neural network

ABSTRACT

A method for analyzing computed tomography angiography (CTA) data is provided. The method includes receiving, at a processor, three-dimensional (3D) CTA data. The method also includes automatically, via the processor, detecting objects of interest within the 3D CTA data. The method further includes generating, via the processor, a CTA image volume that only includes the objects of interest.

BACKGROUND

The subject matter disclosed herein relates to medical imaging and, inparticular, to a system and method for performing automated computedtomography angiography.

Volumetric medical imaging technologies use a variety of techniques togather three-dimensional information about the body. For example,computed tomography (CT) imaging system measure the attenuation of X-raybeams passed through a patient from numerous angles. Based upon thesemeasurements, a computer is able to reconstruct images of the portionsof a patient's body responsible for the radiation attenuation. As willbe appreciated by those skilled in the art, these images are based uponseparate examination of a series of angularly displaced measurements. Itshould be pointed out that a CT system produces data that represent thedistribution of linear attenuation coefficients of the scanned object.The data are then reconstructed to produce an image that is typicallydisplayed on a screen, and may be printed or reproduced on film.

For example, in the field of CT angiography (CTA), vasculature and othercirculatory system structures may be imaged, typically by administrationof a radio-opaque dye prior to imaging. Visualization of the CTA datatypically is performed in a two-dimensional manner, i.e.,slice-by-slice, or in a three-dimensional manner, i.e., volumevisualization, which allows the data to be analyzed for vascularpathologies. For example, the data may be analyzed for aneurysms,vascular calcification, renal donor assessment, stent placement,vascular blockage, and vascular evaluation for sizing and/or runoff.Once a pathology is located, quantitative assessments of the pathologymay be made of the on the original two-dimensional slices.

The CTA process may include processes for segmenting structures in theimage data, such as the vasculature and/or the bone structures. Suchsegmentation typically involves identifying which voxels of the imagedata are associated with a particular structure or structures ofinterest. Segmented structures may then be viewed outside of the contextof the remainder of the image data or may be masked from the remainderof the image data to allow otherwise obstructed structure to be viewed.For example, in CTA, segmentation may be performed to identify allvoxels associated with the vasculature, allowing the entire circulatorysystem in the imaged region to be extracted and viewed. Similarly, allvoxels of the bone structures may be identified and masked, orsubtracted, from the image data, allowing vasculature and/or otherstructures which might otherwise be obscured by the relatively opaquebone structures to be observed during subsequent visualization.

However, segmentation of vasculature and bone structures may becomplicated by a variety of factors. For example, in CTA, overlappingimage intensities, close proximity of imaged structures, limiteddetector resolution, slow imaging volume coverage (i.e., slow scanspeed), calcification, complexity of the anatomic regions andsub-regions, imperfect contrast timing, and interventional devices maymake the identification and segmentation of bone and vascular structuresdifficult. Because of these complicating factors, image visualizationspecialists are utilized to manually intervene to generate images forradiologists. For example, these image visualization specialists bothmanually detect and/or remove structures (e.g., vein, artery, etc.) fromthe reconstructed CT data and reformat (e.g., transform or sample) theimage volume to generate two-dimensional (2D) images. The utilization ofthese image visualization specialist is labor intensive and costly. Inaddition, on lower tier scanners (i.e. less than 16 rows) it isphysically impossible to acquire a vascular study of the arterieswithout contamination of the veins given the required acquisition time.It may, therefore, be desirable to automate the detection and/or removalof structures from the reconstructed CT data as well as reformatting ofthe image volume in the CTA process.

BRIEF DESCRIPTION

Certain embodiments commensurate in scope with the originally claimedsubject matter are summarized below. These embodiments are not intendedto limit the scope of the claimed subject matter, but rather theseembodiments are intended only to provide a brief summary of possibleforms of the subject matter. Indeed, the subject matter may encompass avariety of forms that may be similar to or different from theembodiments set forth below.

In accordance with a first embodiment, a method for analyzing computedtomography angiography (CTA) data is provided. The method includesreceiving, at a processor, three-dimensional (3D) CTA data. The methodalso includes automatically, via the processor, detecting objects ofinterest within the 3D CTA data. The method further includes generating,via the processor, a CTA image volume that only includes the objects ofinterest.

In accordance with a second embodiment, a method for analyzing computedtomography angiography (CTA) data is provided. The method includesreceiving, at a processor, four-dimensional (4D) CTA data. The methodalso includes generating, via the processor, non time-resolved CTA datafrom the 4D CTA data. The method further includes generating, via theprocessor, a first set of 4D images including veins only from the 4D CTAdata. The method still further includes generating, via the processor, asecond set of 4D images including arteries only from the 4D CTA data.The method yet further includes training, via the processor, aconvolutional neural network utilizing the non time-resolved CTA data,the first set of 4D images, and the second set of 4D images to generatea trained convolutional neural network.

In accordance with a third embodiment, a method for analyzing computedtomography angiography (CTA) data is provided. The method includesobtaining, at the processor, past review types utilized by users, imagereformat rendering angles relative to computed tomography (CT) systemlandmarks for a respective past review type selected by the users, andimage reformat rendering angles relative to anatomical landmarks for therespective past review type selected by the users. The method alsoincludes training, via the processor, the convolutional neural networkutilizing the past review types utilized by users, the image reformatrendering angles relative to CT system landmarks for the respective pastreview type selected by the users, and the image reformat renderingangles relative to anatomical landmarks for the respective past reviewtype selected by the users to generate a trained convolutional neuralnetwork.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a block diagram depicting components of a computed tomography(CT) imaging system, in accordance with aspects of the presentdisclosure;

FIG. 2 is a flow chart of an embodiment of a method for analyzingcomputed tomography angiography (CTA) data;

FIG. 3. is a flow chart of an embodiment of a method for training aneural network with four-dimensional (4D) CTA data for utilization indetecting or removing objects from three-dimensional (3D) CTA data;

FIG. 4 is a graphical representation of CTA data for a given voxellocation over time;

FIG. 5 is flow chart of an embodiment of a method for utilizing atrained neural network to detect or remove objects from 3D CTA data;

FIG. 6 is a flow chart of an embodiment of a method for training aneural network for utilization in reformatting an image volume; and

FIG. 7 is a flow chart of an embodiment of a method for utilizing atrained neural network to reformat an image volume.

DETAILED DESCRIPTION

One or more specific embodiments will be described below. In an effortto provide a concise description of these embodiments, all features ofan actual implementation may not be described in the specification. Itshould be appreciated that in the development of any such actualimplementation, as in any engineering or design project, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the present subjectmatter, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.Furthermore, any numerical examples in the following discussion areintended to be non-limiting, and thus additional numerical values,ranges, and percentages are within the scope of the disclosedembodiments.

Disclosed herein are systems and methods for analyzing computedtomography angiography (CTA) data. In particular, the disclosedembodiments utilize processing circuitry (e.g., of a console or computerof a computed tomography (CT) imaging system) to automatically isolate(via detection and/or removal) an object of interest (e.g., vein,artery, soft tissue, bone, etc.) from three-dimensional (3D) CTA dataand to automatically (i.e., without user interaction or input) reformatan imaging volume (e.g., only having the object of interest) to generatetwo-dimensional (2D) images. In certain embodiments, a neural networkmay be trained on four-dimensional (4D) CTA data to learn how toautomatically detect or remove objects from reconstructed 3D CTA data togenerate image volumes. In addition, a neural network may be trained toidentify an object of interest and desired orientation of a particularview based on past review types utilized by users and their respectiveimage reformat rendering angles relative to CT system landmarks and/oranatomical landmarks utilized in those past review types. Theautomatization of the isolation of an object of interest andreformatting of CTA data enables analysis and visualization of CTA dataon lower tier scanners (e.g., with less than 16 row scanners) having aslow volume coverage and/or situations with imperfect contrast timing.In addition, on fast volumetric coverage systems, the disclosedtechniques reduce venous contamination due to imperfect contrast timing.Further, this automatization reduces both the time and costs associatedwith utilizing visualization specialists in generating CTA data foranalysis.

With this in mind, an example of a CT imaging system 10 designed toacquire X-ray attenuation data at a variety of views around a patient(or other subject or object of interest) and suitable for automatedangiography (i.e., automated object identification and reformatting) isprovided in FIG. 1. Although the techniques below are discussed in thecontext of a CT imaging system, the techniques may also be utilized inother imaging systems (e.g., magnetic resonance (MR) imaging system,X-ray system, ultrasound system, positron emission tomography (PET)system, etc.). In the embodiment illustrated in FIG. 1, imaging system10 includes a source of X-ray radiation 12 positioned adjacent to acollimator 14. The X-ray source 12 may be an X-ray tube, a distributedX-ray source (such as a solid-state or thermionic X-ray source) or anyother source of X-ray radiation suitable for the acquisition of medicalor other images.

The collimator 14 permits X-rays 16 to pass into a region in which apatient 18, is positioned. In the depicted example, the X-rays 16 arecollimated to be a cone-shaped beam, i.e., a cone-beam that passesthrough the imaged volume. A portion of the X-ray radiation 20 passesthrough or around the patient 18 (or other subject of interest) andimpacts a detector array, represented generally at reference numeral 22.Detector elements of the array produce electrical signals that representthe intensity of the incident X-rays 20. These signals are acquired andprocessed to reconstruct images of the features within the patient 18.

Source 12 is controlled by a system controller 24, which furnishes bothpower, and control signals for CT examination sequences, includingacquisition of 2D localizer or scout images used to identify anatomy ofinterest within the patient for subsequent scan protocols. In thedepicted embodiment, the system controller 24 controls the source 12 viaan X-ray controller 26 which may be a component of the system controller24. In such an embodiment, the X-ray controller 26 may be configured toprovide power and timing signals to the X-ray source 12.

Moreover, the detector 22 is coupled to the system controller 24, whichcontrols acquisition of the signals generated in the detector 22. In thedepicted embodiment, the system controller 24 acquires the signalsgenerated by the detector using a data acquisition system 28. The dataacquisition system 28 receives data collected by readout electronics ofthe detector 22. The data acquisition system 28 may receive sampledanalog signals from the detector 22 and convert the data to digitalsignals for subsequent processing by a processor 30 discussed below.Alternatively, in other embodiments the digital-to-analog conversion maybe performed by circuitry provided on the detector 22 itself. The systemcontroller 24 may also execute various signal processing and filtrationfunctions with regard to the acquired image signals, such as for initialadjustment of dynamic ranges, interleaving of digital image data, and soforth.

In the embodiment illustrated in FIG. 1, system controller 24 is coupledto a rotational subsystem 32 and a linear positioning subsystem 34. Therotational subsystem 32 enables the X-ray source 12, collimator 14 andthe detector 22 to be rotated one or multiple turns around the patient18, such as rotated primarily in an x, y-plane about the patient. Itshould be noted that the rotational subsystem 32 might include a gantryupon which the respective X-ray emission and detection components aredisposed. Thus, in such an embodiment, the system controller 24 may beutilized to operate the gantry.

The linear positioning subsystem 34 may enable the patient 18, or morespecifically a table supporting the patient, to be displaced within thebore of the CT system 10, such as in the z-direction relative torotation of the gantry. Thus, the table may be linearly moved (in acontinuous or step-wise fashion) within the gantry to generate images ofparticular areas of the patient 18. In the depicted embodiment, thesystem controller 24 controls the movement of the rotational subsystem32 and/or the linear positioning subsystem 34 via a motor controller 36.

In general, system controller 24 commands operation of the imagingsystem 10 (such as via the operation of the source 12, detector 22, andpositioning systems described above) to execute examination protocolsand to process acquired data. For example, the system controller 24, viathe systems and controllers noted above, may rotate a gantry supportingthe source 12 and detector 22 about a subject of interest so that X-rayattenuation data may be obtained at one or more views relative to thesubject. In the present context, system controller 24 may also includesignal processing circuitry, associated memory circuitry for storingprograms and routines executed by the computer (such as routines forexecuting image visualization techniques that enable automatic (i.e.,without user intervention) detection of objects of interests andreformatting of 2D images from an imaging volume as described herein),as well as configuration parameters, image data, reconstructed images,and so forth.

In the depicted embodiment, the image signals acquired and processed bythe system controller 24 are provided to a processing component 30 forreconstruction of images in accordance with the presently disclosedalgorithms. The processing component 30 may be one or more general orapplication-specific microprocessors. The data collected by the dataacquisition system 28 may be transmitted to the processing component 30directly or after storage in a memory 38. Any type of memory suitablefor storing data might be utilized by such an exemplary system 10. Forexample, the memory 38 may include one or more optical, magnetic, and/orsolid-state memory storage structures. Moreover, the memory 38 may belocated at the acquisition system site and/or may include remote storagedevices for storing data, processing parameters, and/or routines forimage reconstruction as described herein.

The processing component 30 may be configured to receive commands andscanning parameters from an operator via an operator workstation 40,typically equipped with a keyboard and/or other input devices. Anoperator may control the system 10 via the operator workstation 40.Thus, the operator may observe the reconstructed images and/or otherwiseoperate the system 10 using the operator workstation 40. For example, adisplay 42 coupled to the operator workstation 40 may be utilized toobserve the reconstructed images and to control imaging. Additionally,the images may also be printed by a printer 44 which may be coupled tothe operator workstation 40.

Further, the processing component 30 and operator workstation 40 may becoupled to other output devices, which may include standard or specialpurpose computer monitors and associated processing circuitry. One ormore operator workstations 40 may be further linked in the system foroutputting system parameters, requesting examinations, viewing images,and so forth. In general, displays, printers, workstations, and similardevices supplied within the system may be local to the data acquisitioncomponents, or may be remote from these components, such as elsewherewithin an institution or hospital, or in an entirely different location,linked to the image acquisition system via one or more configurablenetworks, such as the Internet, virtual private networks, and so forth.

It should be further noted that the operator workstation 40 may also becoupled to a picture archiving and communications system (PACS) 46. PACS46 may in turn be coupled to a remote client 48, radiology departmentinformation system (RIS), hospital information system (HIS) or to aninternal or external network, so that others at different locations maygain access to the raw or processed image data.

While the preceding discussion has treated the various exemplarycomponents of the imaging system 10 separately, these various componentsmay be provided within a common platform or in interconnected platforms.For example, the processing component 30, memory 38, and operatorworkstation 40 may be provided collectively as a general or specialpurpose computer or workstation configured to operate in accordance withthe aspects of the present disclosure. In such embodiments, the generalor special purpose computer may be provided as a separate component withrespect to the data acquisition components of the system 10 or may beprovided in a common platform with such components. Likewise, the systemcontroller 24 may be provided as part of such a computer or workstationor as part of a separate system dedicated to image acquisition.

As discussed herein, the system 10 of FIG. 1 may be used to conduct acomputed tomography (CT) scan to acquire 3D or 4D CTA data from apatient 18 or object. The 4D CTA may be utilized by the system to traina neural network (e.g., convolutional neural network) or machinelearning algorithm to detect objects of interest within 3D CTA data. Inaddition, past activities or review types (and the associated imagereformat rendering angles utilized relative to CT system or anatomicallandmarks) conducted by advanced visualization specialists may beutilized to train the neural network or machine learning algorithm tolearn anatomical locations and reformat planes for utilization inidentifying the location of objects of interests (e.g., vessels) and adesired orientation of view an CTA imaging volume derived from the 3DCTA data. The neural network or machine learning algorithm may enablethe system to automatically detect object of interests from 3D CTA dataand to automatically reformat the CTA imaging volume to generate desired2D images of only the object of interest.

FIG. 2 is a flow chart of an embodiment of a method 50 for analyzing CTAdata. Some or all of the steps of the method 50 may be performed by thesystem controller 24, processing component 30, and/or operatorworkstation 40. One or more steps of the illustrated method 50 mayperformed in a different order from the order depicted in FIG. 2 and/orsimultaneously. The method 50 includes acquiring CT data (e.g., 3D CTAdata) of a patient or object (e.g., utilizing system 10) (block 52). Themethod 50 also includes reconstructing the CT data (block 54).

The method 50 further includes automatically (i.e., without userinteraction) detecting or identifying (e.g., via segmentation) an objectof interest (e.g., artery, vein, bone, or soft tissue) from thereconstructed CT data to generate an image volume of interest (e.g., 3DCTA image volume) (block 56). In certain embodiments, the method 50includes removing other objects other than the object of interest fromthe reconstructed CT data. For example, if the object of interest is anartery, veins, bone, and/or soft tissue may be removed from the imagevolume. The detection and/or removal of objects may be automaticallyexecuted via a trained neural network or machine learning algorithm. Incertain embodiments, the trained neural network may be a convolutionalneural network (CNN) that utilizes cross-correlation in analyzingimaging data. The CNN utilizes different multilayer perceptrons thatrequire minimal preprocessing. As a result, the CNN learns the filtersor weights to be utilized (enabling independence from prior knowledgeand human effort). In addition, the CNN shares weights that are utilizedin the convolutional layers to reduce memory footprint and improveperformance. The training of the neural network for object detection oridentification is described in greater detail below.

The method 50 yet further includes automatically (i.e., without userinteraction) reformatting (i.e., sampling or transforming) or planarreformatting the image volume (e.g., CTA image volume) to generate oneor more 2D images (e.g., for a specific review type) that include onlythe object of interest (block 58). Reformatting may utilize volumerendering, directional maximum intensity projection (MIP), or othervisualization technique in generating the 2D images. The image reformatrendering angles of the 2D images may be set relative to global CTsystem landmarks (e.g., axial, coronal, or sagittal MIPs). In addition,the image reformat rendering angles of the 2D images may be set relativeto anatomical landmarks (e.g., volume rendering of circle of Willis,left carotid, right carotid, etc.). The reformatting or planarreformatting may be automatically executed via a trained neural networkor machine learning algorithm. The training of the neural network forreformatting is described in greater detail below. The method 50 evenfurther includes providing the one or more generated 2D images to PACS(block 60) for visualization (e.g., in a radiologist report).

FIG. 3 is a flow chart of an embodiment of a method 62 for training aneural network 89 with four-dimensional (4D) CTA data for utilization indetecting or removing objects from three-dimensional (3D) CTA data. Someor all of the steps of the method 62 may be performed by the systemcontroller 24, processing component 30, and/or operator workstation 40.One or more steps of the illustrated method 62 may performed in adifferent order from the order depicted in FIG. 3 and/or simultaneously.The method 62 includes acquiring or obtaining 4D CTA data 64 of apatient (e.g., utilizing system 10) (block 66). 4D CTA data includes x,y, and z data in conjunction with time. FIG. 4 is a graphicalrepresentation 68 of CTA data for a given voxel location over time(i.e., 4D CTA data). The graph 68 includes an x-axis 70 representingtime and a y-axis 72 representing CT intensity (e.g., due to thepresence of a contrast agent). CTA may be collected at various times(T₁, T₂, T₃, etc.) for the given voxel location to form the 4D CTA data.Plot 74 represents the signal from artery and plot 76 represents thesignal from the vein. As depicted in FIG. 4, initially (e.g., at T₁) themajority of the contribution to the intensity is from the artery (wheremost of the contrast agent is located). Then, (e.g., at T₂) thecontribution to the intensity is split between both the artery and thevein (due to the presence of the contrast agent in both). Finally,(e.g., at T₃) the majority of the contribution to the intensity is fromthe vein (where most of the contrast agent is located).

The method 62 includes generating a weighted average from acquired orobtained 4D CTA data (x, y, and z data in conjunction with time) (block78). For example, the data points T₁, T₂, and T₃ may be given differentweights, where the normalized weighted sum the weights may equal 1. Incertain embodiments, data points that include the majority of intensityin the artery (e.g., T₁) may be given a higher weight than data pointsthat include the majority of intensity in the vein (e.g., T₃). In otherembodiments, data points that include the majority of intensity in thevein (e.g., T₃) may be given a higher weight than data points thatinclude the majority of intensity in the artery (e.g., T₁). The method62 also includes generating non-time resolved or static 3D CTA image(s)80 with arteries and veins based on the weighted average of the 4D CTAdata (block 82). Non-time resolved images are similar to images acquiredin standard CT acquisition.

The method 62 further includes generating artery 84 and/or vein 86 only4D images from the 4D CTA data (block 88). 4D segmentation techniquesare utilized to generate the artery only images 84 and the vein onlyimages 86. The 4D segmentation techniques identify different classes oftissues (e.g., vein, artery, soft tissue, or bone) in the 4D CTA data.The method 62 even further includes training a neural network 89 (e.g.,CNN as described above) or machine learning algorithm to detect oridentify (or remove) objects of interest (e.g., vein, artery, softtissue, bone) from 3D CTA data (block 90). In certain embodiments, theneural network 89 is trained on the non-time resolved image(s) 80,artery only images 84, and vein only images 86. In other embodiments,the neural network 89 is trained on one or more of the non-time resolvedimage(s) 80, artery only images 84, and vein only images 86. The weightslearned by the trained neural network 89 may be stored for theapplication of the trained neural network 89 to 3D CTA data.

FIG. 5 is flow chart of an embodiment of a method 92 for utilizing thetrained neural network 89 to detect or remove objects from 3D CTA data.Some or all of the steps of the method 92 may be performed by the systemcontroller 24, processing component 30, and/or operator workstation 40.The method 92 includes applying the trained neural network 89 to theacquired 3D CTA data 94 from the patient (block 96). As noted above, thetrained neural network 89 may utilize the weights learned duringtraining to the 3D CTA data. The method 92 also includes automaticallydetecting or identifying (or removing) objects from the 3D CTA data (viathe applied trained neural network 89) to generate a 3D CTA image volume98 that only includes the object of interest (e.g., vein, artery, softtissue, bone) (block 100).

FIG. 6 is a flow chart of an embodiment of a method 102 for training aneural network 104 for utilization in reformatting (e.g., planarreformatting) an image volume. Some or all of the steps of the method 92may be performed by the system controller 24, processing component 30,and/or operator workstation 40. For a given CT protocol and review type,advanced visualization specialists or users manually determine imagereformat rendering angles for an object interest in an image volume. Inparticular, the advanced visualization specialists set the imagereformat rendering angles relative CT system landmarks (e.g., axial,coronal, and/or sagittal MIPs) and/or image reformat rendering anglesrelative to anatomical landmarks (e.g., volume rendering of the Circleof Willis, volume rendering of the left carotid, volume rendering of theright carotid, etc.) in generating the 2D images with only the object ofinterest. Past review types 106, associated image reformat renderingangles 108 relative to system landmarks for these respective past reviewtypes, associated image reformat rendering angles 110 relative toanatomical landmarks for these respective past review types, and the 3DCTA data (imaging volumes) 112 utilized in these past review types maybe monitored and stored for utilization in training the neural network104 (e.g., CNN) or machine learning algorithm. The method 102 includesobtaining these past review types 106 and associated information (e.g.,associated image reformat rendering angles 108, 110 and/or associated 3DCTA data 112) (block 114). The method 102 also includes training theneural network 104 (e.g., CNN) with past review types 106 image reformatrendering angles 108, 110, and/or associated 3D CTA data 112 (block116). The neural network 104 learns anatomical locations and reformatplanes as well identifies a location of an object of interest (e.g.,vessel of interest) and the desired orientation of the view based on thereview type. Thus, the trained neural network 104 when applied canautomatically set the image reformat rendering angles relative to systemlandmarks and anatomical landmarks based on the CT protocol and reviewtype.

FIG. 7 is a flow chart of an embodiment of a method 118 for utilizingthe trained neural network 104 to reformat an image volume. Some or allof the steps of the method 92 may be performed by the system controller24, processing component 30, and/or operator workstation 40. The method118 includes applying the trained neural network 104 to an image volume(e.g., acquired 3D CTA data 94 from the patient) (block 122). The method118 also includes automatically reformatting or planar reformatting theimage volume (via the applied trained neural network 104) to generate 2DCTA images 124 that only include the object of interest (e.g., artery,vein, bone, soft tissue) for the CT protocol and review type (block126).

Technical effects of the disclosed embodiments include providing systemsand methods that automatically isolate (via detection and/or removal) anobject of interest (e.g., vein, artery, soft tissue, bone, etc.) from 3DCTA data and automatically (i.e., without user interaction or input)reformat an imaging volume (e.g., only having the object of interest) togenerate 2D CTA images. The automatization of the isolation of an objectof interest and reformatting of CTA data enables analysis andvisualization of CTA data on lower tier scanners (e.g., with less than16 row scanners) having a slow volume coverage and/or situations withimperfect contrast timing. In addition, on fast volumetric coveragesystems, the disclosed techniques reduce venous contamination due toimperfect contrast timing. Further, this automatization reduces both thetime and costs associated with utilizing visualization specialists ingenerating CTA data for analysis.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

The invention claimed is:
 1. A method for analyzing computed tomographyangiography (CTA) data, comprising: obtaining, at a processor, pastreview types utilized by users, image reformat rendering angles relativeto computed tomography (CT) system landmarks for a respective pastreview type selected by the users, and image reformat rendering anglesrelative to anatomical landmarks for the respective past review typeselected by the users; training, via the processor, a convolutionalneural network utilizing the past review types utilized by users, theimage reformat rendering angles relative to CT system landmarks for therespective past review type selected by the users, and the imagereformat rendering angles relative to anatomical landmarks for therespective past review type selected by the users to generate thetrained convolutional neural network; receiving, at the processor,three-dimensional (3D) CTA data; automatically, via the processor,detecting objects of interest within the 3D CTA data, whereinautomatically detecting the objects of interest within the CTA datacomprises applying, via the processor, the trained convolutional neuralnetwork to the 3D CTA data to segment the objects of interest from the3D CTA data; generating, via the processor, a CTA image volume that onlyincludes the objects of interest; and utilizing, via the processor, thetrained convolutional neural network to automatically reformat the CTAimage volume to generate one or more two-dimensional (2D) CTA images. 2.The method of claim 1, wherein the objects of interest comprisearteries, veins, soft tissue, or bone.
 3. The method of claim 1,comprising: receiving, at the processor, four-dimensional (4D) CTA data;generating, via the processor, non time-resolved CTA data from the 4DCTA data; generating, via the processor, a first set of 4D imagesincluding veins only from the 4D CTA data; and generating, via theprocessor, a second set of 4D images including arteries only from the 4DCTA data.
 4. The method of claim 3, comprising training, via theprocessor, a convolutional neural network utilizing the nontime-resolved CTA data, the first set of 4D images, and the second setof 4D images to generate the trained convolutional neural network. 5.The method of claim 3, comprising training, via the processor, aconvolutional neural network utilizing the non time-resolved CTA data,the first set of 4D images, or the second set of 4D images to generatethe trained convolutional neural network.
 6. The method of claim 3,wherein generating the non time-resolved CTA data comprises applying,via the processor, a weighted average to the 4D CTA data.
 7. The methodof claim 3, wherein generating the first and second sets of 4D imagescomprises performing, via the processor, 4D segmentation on the 4D CTAdata.
 8. The method of claim 1, wherein the trained convolutional neuralnetwork, via the processor, in reformatting the CTA image volumeidentifies an anatomical location of the objects of interest within theCTA image volume and determines a desired orientation of the one or more2D CTA images.
 9. A method for analyzing computed tomography angiography(CTA) data, comprising: receiving, at a processor, four-dimensional (4D)CTA data; generating, via the processor, non time-resolved CTA data fromthe 4D CTA data; generating, via the processor, a first set of 4D imagesincluding veins only from the 4D CTA data; generating, via theprocessor, a second set of 4D images including arteries only from the 4DCTA data; and training, via the processor, a convolutional neuralnetwork utilizing the non time-resolved CTA data, the first set of 4Dimages, and the second set of 4D images to generate a trainedconvolutional neural network.
 10. The method of claim 9, comprising:receiving, at the processor, three-dimensional (3D) CTA data;automatically, via the processor, detecting objects of interest withinthe 3D CTA data by applying the trained convolutional neural network tothe 3D CTA data to segment the objects of interest from the CTA data;and generating, via the processor, a CTA image volume that only includesthe objects of interest.
 11. The method of claim 10, comprisingautomatically, via the processor, reformatting the CTA image volume togenerate one or more two-dimensional (2D) CTA images.
 12. The method ofclaim 11, wherein automatically reformatting the CTA image volumecomprises applying, via the processor, the trained convolutional neuralnetwork to the CTA image volume to reformat the CTA image volume. 13.The method of claim 12, comprising: obtaining, at the processor, pastreview types utilized by users, image reformat rendering angles relativeto computed tomography (CT) system landmarks for a respective pastreview type selected by the users, and image reformat rendering anglesrelative to anatomical landmarks for the respective past review typeselected by the users; and training, via the processor, theconvolutional neural network utilizing the past review types utilized byusers, the image reformat rendering angles relative to CT systemlandmarks for the respective past review type selected by the users, andthe image reformat rendering angles relative to anatomical landmarks forthe respective past review type selected by the users to generate thetrained convolutional neural network.
 14. A method for analyzingcomputed tomography angiography (CTA) data, comprising: obtaining, atthe processor, past review types utilized by users, image reformatrendering angles relative to computed tomography (CT) system landmarksfor a respective past review type selected by the users, and imagereformat rendering angles relative to anatomical landmarks for therespective past review type selected by the users; and training, via theprocessor, a convolutional neural network utilizing the past reviewtypes utilized by users, the image reformat rendering angles relative toCT system landmarks for the respective past review type selected by theusers, and the image reformat rendering angles relative to anatomicallandmarks for the respective past review type selected by the users togenerate a trained convolutional neural network.
 15. The method of claim14, comprising automatically, via the processor, reformatting a CTAimage volume to generate one or more two-dimensional (2D) CTA images byapplying the trained convolutional neural network to the CTA imagevolume, wherein the CTA image volume only includes objects of interestsegmented from three-dimensional (3D) CTA data.
 16. The method of claim15, wherein the trained convolutional neural network, via the processor,in reformatting the CTA image volume identifies an anatomical locationof the object of interest within the CTA image volume and determines adesired orientation of the one or more 2D CTA images.
 17. The method ofclaim 15, comprising: receiving, at the processor, the 3D CTA data;automatically, via the processor, detecting the objects of interestwithin the 3D CTA data by applying the trained convolutional neuralnetwork to the 3D CTA data to segment the objects of interest from theCTA data; and generating, via the processor, the CTA image volume thatonly includes the objects of interest.
 18. The method of claim 17,comprising: receiving, at the processor, four-dimensional (4D) CTA data;generating, via the processor, non time-resolved CTA data from the 4DCTA data; generating, via the processor, a first set of 4D imagesincluding veins only from the 4D CTA data; generating, via theprocessor, a second set of 4D images including arteries only from the 4DCTA data; and training, via the processor, the convolutional neuralnetwork utilizing the non time-resolved CTA data, the first set of 4Dimages, and the second set of 4D images to generate the trainedconvolutional neural network.